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In [ ]:
import sys
import os
from importlib_metadata import distribution
pwd = os.getcwd()
modellingpath = pwd.rpartition("modelling")[0] + pwd.rpartition("modelling")[1] 
sys.path.append(modellingpath + '/lib')
#############

from numerical.cn_edgegrowth2_numba import cn_edgegrowth2 as cn_edgegrowth2_numba
from numerical.cn_edgegrowth2 import cn_edgegrowth2
from numerical.cn_nogrowth import cn_nogrowth
from numerical.cn_plot import plot1D, surfpattern
import pickle
import matplotlib.pyplot as plt
import time
import numpy as np
from tqdm import tqdm
#system parameters

circuit_n = 'turinghill'
variant=0 
n_param_sets = 2000000
# par_dict = {'c1':0.1, 'c2':1,'c3':0.9,'c4':1, 'd_A': 1, 'd_B':10}

df= pickle.load( open(modellingpath + "/growth/out/analytical/turing/turing_df_%s_variant%r_%rparametersets.pkl"%(circuit_n,variant,n_param_sets), "rb"))
# df= pickle.load( open(modellingpath + "/growth/out/analytical/instability/instability_df_%s_variant%r_%rparametersets.pkl"%(circuit_n,variant,n_param_sets), "rb"))

df.index.names = ['parID','ss']
# df = multiple_df.xs(0, level=1)
#solver parameters




L=500; x_gridpoints=1; J=L*x_gridpoints;I=J 


T=3000; t_gridpoints = 5; N=T*t_gridpoints #Number of timepoints <below 3 is bad if x_gridpoints=1

boundaryCoeff=2;rate=0.1
print(len(df))
filename= lambda mechanism, parID: 'circuit%s_variant%s_bc%s_%s_rate%s_ID%s_L%r_J%r_T%r_N%r'%(circuit_n,variant,boundaryCoeff, mechanism,rate,parID,L,J,T,N)
for parID,ss in tqdm(df.index):
    parIDss = f'{parID}.{ss}'
    print(parIDss)
    mechanism = 'edgegrowth2'
    par_dict = df.loc[(parID,ss)].to_dict()
    print(par_dict)
    U_final,U_record, U0, x_grid, reduced_t_grid, cellMatrix= cn_edgegrowth2_numba(par_dict,L,J,T,N, circuit_n, rate=rate, boundaryCoeff=boundaryCoeff, tqdm_disable=True)
    pickle.dump(U_final, open(modellingpath + '/growth/out/numerical/%s/%s/simulation/2Dfinal_%s.pkl'%(circuit_n,mechanism,filename(mechanism,parIDss)), 'wb'))
    pickle.dump(U_record, open(modellingpath + '/growth/out/numerical/%s/%s/simulation/2Drecord_%s.pkl'%(circuit_n,mechanism,filename(mechanism,parIDss)), 'wb'))
    plot1D(U_final)
    plt.show()
    mechanism = 'nogrowth'
    U_final,U_record, U0, x_grid, reduced_t_grid= cn_nogrowth(par_dict,L,J,T,N, circuit_n, tqdm_disable=True)
    pickle.dump(U_final, open(modellingpath + '/growth/out/numerical/%s/%s/simulation/2Dfinal_%s.pkl'%(circuit_n,mechanism,filename(mechanism,parIDss)), 'wb'))
    pickle.dump(U_record, open(modellingpath + '/growth/out/numerical/%s/%s/simulation/2Drecord_%s.pkl'%(circuit_n,mechanism,filename(mechanism,parIDss)), 'wb'))
    plot1D(U_final)
    plt.show()
# #run
# no_numba = False
# if no_numba == True:
#     st = time.time()
#     U,U_record, U0, x_grid, reduced_t_grid, cellMatrix= cn_edgegrowth2(par_dict,L,J,T,N, circuit_n, rate=0.1, boundaryCoeff=2)
#     elapsed_time = time.time() - st
#     print('Execution time numba:', time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
#     plt.scatter(x_grid,cellMatrix)
#     plt.show()
#     #plot
#     plot1D(U, savefig=False,filename='')
#     plt.show()
#     surfpattern(U_record, [x_grid, reduced_t_grid], 'linear',morphogen=0, rate=0, savefig=False,filename='',logResults=False,normalize=False)
#     plt.show()
#     surfpattern(U_record, [x_grid, reduced_t_grid], 'linear',  morphogen=1, rate=0, savefig=False,filename='',logResults=False,normalize=False)
#     plt.show()
# numba=False
# if numba == True: 
#     st = time.time()
#     U,U_record, U0, x_grid, reduced_t_grid, cellMatrix= cn_edgegrowth2_numba(par_dict,L,J,T,N, circuit_n, rate=0.1, boundaryCoeff=2)
#     elapsed_time = time.time() - st
#     print('Execution time:', time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
# no_growth = True
# if no_growth == True:
#     st = time.time()
#     U,U_record, U0, x_grid, reduced_t_grid= cn_nogrowth(par_dict,L,J,T,N, circuit_n, tqdm_disable=False)
#     elapsed_time = time.time() - st
#     print('Execution time:', time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
#     #plot
#     plot1D(U, savefig=False,filename='')
#     plt.show()
#     surfpattern(U_record, [x_grid, reduced_t_grid], 'linear',morphogen=0, rate=0, savefig=False,filename='',logResults=False,normalize=False)
#     plt.show()
#     surfpattern(U_record, [x_grid, reduced_t_grid], 'linear',  morphogen=1, rate=0, savefig=False,filename='',logResults=False,normalize=False)
#     plt.show()
# print(np.sum(U_final[0]))

  0%|          | 0/31 [00:00<?, ?it/s]
31
118941.0
{'ba': 0.01, 'bb': 0.01, 'Va': 815.2281990625947, 'Vb': 869.092720545832, 'kaa': 12.891931764200102, 'kba': 42.952283653646234, 'kab': 1.6989749233704032, 'kbb': 184.85059192663672, 'mua': 1.2913151609791507, 'mub': 1.3882435185207276, 'd_B': 9.871792232811737, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([ 0.36781896, 28.03584002]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.5281387272214166+0j), 'complex_dispersion': True}
negative

  3%|▎         | 1/31 [00:14<07:10, 14.35s/it]
130517.2
{'ba': 0.01, 'bb': 0.01, 'Va': 114.2943824225333, 'Vb': 135.5191790082118, 'kaa': 31.039471979008834, 'kba': 8.382989354562715, 'kab': 23.895203185186315, 'kbb': 0.680626361413112, 'mua': 0.10629343462009974, 'mub': 0.09833370115985028, 'd_B': 8.061394127036298, 'd_A': 1.0, 'n': 2.0, 'ss_n': 4.0, 'ss_list': array([1.99812735, 9.67041749]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.002129146044943686+0j), 'complex_dispersion': True}
negative

  6%|▋         | 2/31 [00:25<06:30, 13.45s/it]
135128.1
{'ba': 0.01, 'bb': 0.01, 'Va': 27.564908966425694, 'Vb': 326.25772039061815, 'kaa': 1.7568405193731178, 'kba': 1.4857913308384587, 'kab': 3.6312436959757273, 'kbb': 58.459387568413824, 'mua': 1.5994924753282964, 'mub': 1.819611424644346, 'd_B': 8.272981979622703, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([0.29640735, 1.19224465]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.23246594566876855+0j), 'complex_dispersion': True}
negative

 10%|▉         | 3/31 [00:36<05:55, 12.70s/it]
350981.4
{'ba': 0.01, 'bb': 0.01, 'Va': 239.16045466671721, 'Vb': 34.63637728615317, 'kaa': 36.2339505371141, 'kba': 64.48723450348534, 'kab': 2.2029491724002734, 'kbb': 3.8503227579228367, 'mua': 0.176897911154491, 'mub': 0.21967063862747171, 'd_B': 9.765606067430125, 'd_A': 1.0, 'n': 2.0, 'ss_n': 5.0, 'ss_list': array([ 1.51834334, 50.82511564]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.06622513286767573+0j), 'complex_dispersion': True}
negative

 13%|█▎        | 4/31 [00:48<05:34, 12.39s/it]
351953.1
{'ba': 0.01, 'bb': 0.01, 'Va': 54.18484104015259, 'Vb': 47.25741550983462, 'kaa': 11.605058993398924, 'kba': 33.85514282492236, 'kab': 4.32564142907592, 'kbb': 57.999664182403706, 'mua': 0.408073564739178, 'mub': 0.4027145677064975, 'd_B': 8.87692980159737, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([ 3.98772768, 53.936696  ]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.028127901418268264+0j), 'complex_dispersion': True}
negative

 16%|█▌        | 5/31 [01:01<05:27, 12.58s/it]
360416.1
{'ba': 0.01, 'bb': 0.01, 'Va': 385.5088897168174, 'Vb': 110.22121523587622, 'kaa': 2.483706697966257, 'kba': 0.9711051957031617, 'kab': 0.40104277427424595, 'kbb': 42.103036140718736, 'mua': 5.250474461283016, 'mub': 14.083381020752846, 'd_B': 7.772167659674627, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([0.12386348, 0.68225491]), 'ss_class': 'stable point', 'system_class': 'turing I', 'maxeig': (0.17701025304920037+0j), 'complex_dispersion': False}
negative
negative
negative

 19%|█▉        | 6/31 [01:15<05:26, 13.06s/it]
472883.2
{'ba': 0.01, 'bb': 0.01, 'Va': 290.6327418040253, 'Vb': 11.45621109841897, 'kaa': 14.439605207063384, 'kba': 0.7407539897652289, 'kab': 8.226865404352063, 'kbb': 160.62343879687893, 'mua': 3.3465368714702333, 'mub': 3.9901290059736207, 'd_B': 6.620820479626943, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([3.94695606, 0.5397153 ]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.5550199052762483+0j), 'complex_dispersion': True}

 23%|██▎       | 7/31 [01:26<04:58, 12.46s/it]
486068.2
{'ba': 0.01, 'bb': 0.01, 'Va': 997.2903282608314, 'Vb': 43.111126294326105, 'kaa': 155.8052786664667, 'kba': 5.559437646546131, 'kab': 17.475798599213125, 'kbb': 0.11000134341052847, 'mua': 0.048235757764698135, 'mub': 0.0802045896337645, 'd_B': 7.693765988716828, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([1.36567456, 3.38727262]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.0021226246628133216+0j), 'complex_dispersion': True}
negative

 26%|██▌       | 8/31 [01:40<04:54, 12.80s/it]
546109.1
{'ba': 0.01, 'bb': 0.01, 'Va': 202.03958750174837, 'Vb': 34.49501460261308, 'kaa': 3.375462276950734, 'kba': 20.057786219077514, 'kab': 0.4415101363086828, 'kbb': 37.15703655950709, 'mua': 0.18011977945255836, 'mub': 0.14115099075969806, 'd_B': 5.773968725763372, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([  0.59267954, 157.23763183]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.013721466808561122+0j), 'complex_dispersion': True}

 29%|██▉       | 9/31 [01:52<04:41, 12.78s/it]
742006.2
{'ba': 0.01, 'bb': 0.01, 'Va': 115.40492671282917, 'Vb': 35.75298038735518, 'kaa': 2.3843297731692674, 'kba': 3.9720414027865862, 'kab': 0.22270580063721748, 'kbb': 2.4367359875485, 'mua': 1.1586282735305444, 'mub': 1.2757264967083521, 'd_B': 5.211852873994694, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([0.0676013 , 2.37223273]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.16226784711775366+0j), 'complex_dispersion': True}

 32%|███▏      | 10/31 [02:04<04:21, 12.44s/it]
862004.2
{'ba': 0.01, 'bb': 0.01, 'Va': 15.342934792094486, 'Vb': 754.2975569594662, 'kaa': 1.6557933391612487, 'kba': 8.439693370086474, 'kab': 6.603317355743912, 'kbb': 8.006935541273098, 'mua': 2.7471878890929213, 'mub': 2.1766960056095455, 'd_B': 6.436259387327192, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([0.74958926, 4.41325274]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.4247302804824109+0j), 'complex_dispersion': True}

 35%|███▌      | 11/31 [02:15<04:02, 12.13s/it]
1121752.2
{'ba': 0.01, 'bb': 0.01, 'Va': 35.09596281919261, 'Vb': 42.009523487375624, 'kaa': 204.67025945320665, 'kba': 129.52996179175673, 'kab': 196.2069645904714, 'kbb': 0.2434143443237951, 'mua': 0.02061094760029868, 'mub': 0.020158654231861257, 'd_B': 7.5919466938069515, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([ 49.09562577, 123.28756485]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.0008073348759318894+0j), 'complex_dispersion': True}
negative

 39%|███▊      | 12/31 [02:28<03:51, 12.18s/it]
1133916.0
{'ba': 0.01, 'bb': 0.01, 'Va': 25.28034579814929, 'Vb': 24.820602749573567, 'kaa': 0.46665031483665004, 'kba': 2.894328309523785, 'kab': 0.17150018214289278, 'kbb': 22.58855246886253, 'mua': 2.3401280586314592, 'mub': 1.8123012418852684, 'd_B': 9.29657417053512, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([0.20345611, 8.01213494]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.0951403612171493+0j), 'complex_dispersion': True}
negative

 42%|████▏     | 13/31 [02:40<03:38, 12.15s/it]
1195022.4
{'ba': 0.01, 'bb': 0.01, 'Va': 611.4433330118857, 'Vb': 65.41842217773055, 'kaa': 3.679403701088877, 'kba': 4.9161447235193645, 'kab': 0.2918676371684259, 'kbb': 23.16548966879861, 'mua': 2.681935721389821, 'mub': 3.6395321051690397, 'd_B': 3.8688919677210523, 'd_A': 1.0, 'n': 2.0, 'ss_n': 5.0, 'ss_list': array([ 0.40738494, 11.88045562]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.143157693219905+0j), 'complex_dispersion': True}

 45%|████▌     | 14/31 [02:52<03:26, 12.14s/it]
1251569.4
{'ba': 0.01, 'bb': 0.01, 'Va': 78.3363322679065, 'Vb': 335.81879962648486, 'kaa': 4.829453494045339, 'kba': 130.86352922113667, 'kab': 0.6996888224659922, 'kbb': 126.26882930476437, 'mua': 0.49939894255688555, 'mub': 0.6586702772927231, 'd_B': 8.65380960982406, 'd_A': 1.0, 'n': 2.0, 'ss_n': 5.0, 'ss_list': array([  0.68858169, 250.85811902]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.04685998948372605+0j), 'complex_dispersion': True}
negative

 48%|████▊     | 15/31 [03:04<03:14, 12.17s/it]
1271426.4
{'ba': 0.01, 'bb': 0.01, 'Va': 190.22562841368324, 'Vb': 293.57445146053476, 'kaa': 6.800965172133764, 'kba': 0.2207350784378043, 'kab': 246.46224222918642, 'kbb': 0.3525542163110288, 'mua': 0.05496767705865642, 'mub': 0.006625831542775947, 'd_B': 7.974703275984807, 'd_A': 1.0, 'n': 2.0, 'ss_n': 6.0, 'ss_list': array([0.41344634, 1.62368288]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.0014508345841911796+0j), 'complex_dispersion': True}
negative

 52%|█████▏    | 16/31 [03:17<03:06, 12.44s/it]
1282460.3
{'ba': 0.01, 'bb': 0.01, 'Va': 137.40868705731026, 'Vb': 46.23022959713599, 'kaa': 4.385335909142999, 'kba': 22.875973534242117, 'kab': 0.2523078622811597, 'kbb': 0.7088325932748427, 'mua': 0.6043877303369629, 'mub': 0.5952921481552054, 'd_B': 7.041691131306341, 'd_A': 1.0, 'n': 2.0, 'ss_n': 6.0, 'ss_list': array([ 0.18338943, 26.86238075]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.05459776834667229+0j), 'complex_dispersion': True}

 55%|█████▍    | 17/31 [03:30<02:55, 12.56s/it]
1363184.1
{'ba': 0.01, 'bb': 0.01, 'Va': 489.8671005258825, 'Vb': 792.6915956318819, 'kaa': 23.23039953009945, 'kba': 8.414381315042213, 'kab': 44.27483619151162, 'kbb': 60.43230607871443, 'mua': 4.384668940225246, 'mub': 4.435087240485868, 'd_B': 8.081678478090167, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([8.33716782, 6.12283299]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.3926141426465968+0j), 'complex_dispersion': True}
negative

 58%|█████▊    | 18/31 [03:46<02:56, 13.59s/it]
1365378.2
{'ba': 0.01, 'bb': 0.01, 'Va': 47.893378801210034, 'Vb': 303.5862850568044, 'kaa': 12.846916651306918, 'kba': 59.29856210446242, 'kab': 4.40650368007774, 'kbb': 2.4980566368646926, 'mua': 0.17139154270706858, 'mub': 0.2410507179492761, 'd_B': 4.899399859365768, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([ 0.79077203, 39.33513345]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.0023076793524887673+0j), 'complex_dispersion': True}

 61%|██████▏   | 19/31 [04:01<02:48, 14.03s/it]
1511110.2
{'ba': 0.01, 'bb': 0.01, 'Va': 197.01617697598112, 'Vb': 14.839225143838895, 'kaa': 21.290596844116017, 'kba': 70.58163906952495, 'kab': 1.5274893270274876, 'kbb': 213.0615544604696, 'mua': 0.02646202856244844, 'mub': 0.022356226626566965, 'd_B': 6.5775044835814525, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([  2.45236646, 478.67726623]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.0031422137425548696+0j), 'complex_dispersion': True}

 65%|██████▍   | 20/31 [04:19<02:47, 15.19s/it]
1591223.2
{'ba': 0.01, 'bb': 0.01, 'Va': 181.44888622132726, 'Vb': 124.23883449762988, 'kaa': 1.9077517466192115, 'kba': 0.9491899474542316, 'kab': 1.236091786253191, 'kbb': 0.7330658560779242, 'mua': 8.468121211484974, 'mub': 8.174363578560122, 'd_B': 4.305125014827109, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([0.28267662, 0.75656438]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.281755258396565+0j), 'complex_dispersion': True}

 68%|██████▊   | 21/31 [04:31<02:21, 14.12s/it]
1690913.2
{'ba': 0.01, 'bb': 0.01, 'Va': 352.7320005286167, 'Vb': 181.5972063730003, 'kaa': 22.845293171242496, 'kba': 169.51933721889947, 'kab': 1.3244300604518457, 'kbb': 0.3287189111431211, 'mua': 0.2841278375986933, 'mub': 0.2722956078114015, 'd_B': 9.110463685725591, 'd_A': 1.0, 'n': 2.0, 'ss_n': 5.0, 'ss_list': array([  0.71352878, 150.06077262]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.0850329728032928+0j), 'complex_dispersion': True}
negative

 71%|███████   | 22/31 [04:42<01:58, 13.19s/it]
1695603.2
{'ba': 0.01, 'bb': 0.01, 'Va': 31.97744639010365, 'Vb': 355.74700225932463, 'kaa': 0.5949376437041896, 'kba': 0.1548002535733322, 'kab': 3.0446652198761677, 'kbb': 0.19553009847235556, 'mua': 7.723634366609317, 'mub': 7.413257117473428, 'd_B': 8.454430875350083, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([0.16170534, 0.13633008]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.6959259882071223+0j), 'complex_dispersion': True}
negative

 74%|███████▍  | 23/31 [04:53<01:40, 12.53s/it]
1706579.2
{'ba': 0.01, 'bb': 0.01, 'Va': 59.65913434905526, 'Vb': 58.37513321525424, 'kaa': 19.49110411915976, 'kba': 58.35774236702684, 'kab': 3.4847416149037334, 'kbb': 203.24584674616693, 'mua': 0.08457531491226752, 'mub': 0.07481531387993656, 'd_B': 9.425369788961964, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([ 1.02808973, 62.60937462]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.0047861200244455615+0j), 'complex_dispersion': True}
negative

 77%|███████▋  | 24/31 [05:04<01:24, 12.07s/it]
1710204.2
{'ba': 0.01, 'bb': 0.01, 'Va': 216.47211636902944, 'Vb': 83.96995474560133, 'kaa': 13.679493144396407, 'kba': 3.4999455377566093, 'kab': 12.575575628343543, 'kbb': 14.762696556376671, 'mua': 3.986566405021081, 'mub': 6.215158423693362, 'd_B': 7.634227219284631, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([5.23818164, 1.99913804]), 'ss_class': 'stable point', 'system_class': 'turing I', 'maxeig': (0.5528654870556391+0j), 'complex_dispersion': False}
negative

 81%|████████  | 25/31 [05:15<01:10, 11.76s/it]
1808247.2
{'ba': 0.01, 'bb': 0.01, 'Va': 115.56024514084763, 'Vb': 388.29979364710476, 'kaa': 6.223902031641141, 'kba': 2.5766935598568312, 'kab': 12.162629164481306, 'kbb': 66.47320647479549, 'mua': 2.598519397485629, 'mub': 2.727407326346441, 'd_B': 6.89360915836798, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([1.41924552, 1.91617636]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.3801366210914452+0j), 'complex_dispersion': True}

 84%|████████▍ | 26/31 [05:26<00:57, 11.52s/it]
1809921.2
{'ba': 0.01, 'bb': 0.01, 'Va': 63.3691018173114, 'Vb': 364.64271636876924, 'kaa': 3.141309596623848, 'kba': 138.10754774972156, 'kab': 0.5698650306238873, 'kbb': 1.727839717663489, 'mua': 0.6103431019026028, 'mub': 0.6230432044735008, 'd_B': 6.612335350387968, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([  0.64775478, 329.93235774]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.05649640258731825+0j), 'complex_dispersion': True}

 87%|████████▋ | 27/31 [05:37<00:45, 11.35s/it]
1814923.2
{'ba': 0.01, 'bb': 0.01, 'Va': 53.56055927437798, 'Vb': 13.54273573117521, 'kaa': 2.373041388976948, 'kba': 1.1604487885770782, 'kab': 0.8192073670062823, 'kbb': 1.5034720703746312, 'mua': 2.4032596628365006, 'mub': 2.9870937016665025, 'd_B': 7.673754531362256, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([0.38006234, 0.80634261]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.7280681395694426+0j), 'complex_dispersion': True}
negative

 90%|█████████ | 28/31 [05:48<00:33, 11.27s/it]
1826994.2
{'ba': 0.01, 'bb': 0.01, 'Va': 41.291370463081314, 'Vb': 10.931651652967993, 'kaa': 8.881407254967614, 'kba': 0.5957948694497539, 'kab': 18.624381839430516, 'kbb': 0.23540646060770318, 'mua': 1.2075381545788222, 'mub': 1.2521407267897737, 'd_B': 8.378671879819821, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([4.05409146, 0.40294386]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.018427239301447518+0j), 'complex_dispersion': True}
negative

 94%|█████████▎| 29/31 [05:59<00:22, 11.14s/it]
1869288.0
{'ba': 0.01, 'bb': 0.01, 'Va': 727.246993009074, 'Vb': 34.30308217612897, 'kaa': 4.047834669345688, 'kba': 1.4697684317594815, 'kab': 0.2123372822054281, 'kbb': 1.2004593182769858, 'mua': 1.2932608263221428, 'mub': 0.8170943519827769, 'd_B': 9.67518351441563, 'd_A': 1.0, 'n': 2.0, 'ss_n': 1.0, 'ss_list': array([0.03357005, 1.03598035]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.1400827771050075+0j), 'complex_dispersion': True}
negative

 97%|█████████▋| 30/31 [06:09<00:11, 11.06s/it]
1874504.1
{'ba': 0.01, 'bb': 0.01, 'Va': 122.94933403153534, 'Vb': 29.388223066040922, 'kaa': 27.486707820855447, 'kba': 26.80106743743048, 'kab': 4.632377342389826, 'kbb': 5.63766610052431, 'mua': 0.21246309976977518, 'mub': 0.272714419860516, 'd_B': 9.238315564038194, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([ 2.79687796, 28.82539555]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.037788252039048675+0j), 'complex_dispersion': True}
negative

100%|██████████| 31/31 [06:20<00:00, 12.29s/it]
In [ ]:
import sys
import os
from importlib_metadata import distribution
pwd = os.getcwd()
modellingpath = pwd.rpartition("modelling")[0] + pwd.rpartition("modelling")[1] 
sys.path.append(modellingpath + '/lib')
#############

from numerical.cn_edgegrowth2_numba import cn_edgegrowth2 as cn_edgegrowth2_numba
from numerical.cn_edgegrowth2 import cn_edgegrowth2
from numerical.cn_nogrowth import cn_nogrowth
from numerical.cn_plot import plot1D, surfpattern
import pickle
import matplotlib.pyplot as plt
import time
import numpy as np
from tqdm import tqdm
#system parameters

circuit_n = 'turinghill'
variant=0 
n_param_sets = 2000000
# par_dict = {'c1':0.1, 'c2':1,'c3':0.9,'c4':1, 'd_A': 1, 'd_B':10}

df= pickle.load( open(modellingpath + "/growth/out/analytical/turing/turing_df_%s_variant%r_%rparametersets.pkl"%(circuit_n,variant,n_param_sets), "rb"))
# df= pickle.load( open(modellingpath + "/growth/out/analytical/instability/instability_df_%s_variant%r_%rparametersets.pkl"%(circuit_n,variant,n_param_sets), "rb"))

df.index.names = ['parID','ss']
# df = multiple_df.xs(0, level=1)
#solver parameters




L=50; x_gridpoints=1; J=L*x_gridpoints;I=J 


T=3000; t_gridpoints = 5; N=T*t_gridpoints #Number of timepoints <below 3 is bad if x_gridpoints=1

boundaryCoeff=2;rate=0.01
print(len(df))
filename= lambda mechanism, parID: 'circuit%s_variant%s_bc%s_%s_rate%s_ID%s_L%r_J%r_T%r_N%r'%(circuit_n,variant,boundaryCoeff, mechanism,rate,parID,L,J,T,N)
for parID,ss in tqdm(df.index):
    parIDss = f'{parID}.{ss}'
    print(parIDss)
    mechanism = 'edgegrowth2'
    par_dict = df.loc[(parID,ss)].to_dict()
    print(par_dict)
    U_final,U_record, U0, x_grid, reduced_t_grid, cellMatrix= cn_edgegrowth2_numba(par_dict,L,J,T,N, circuit_n, rate=rate, boundaryCoeff=boundaryCoeff, tqdm_disable=True)
    pickle.dump(U_final, open(modellingpath + '/growth/out/numerical/%s/%s/simulation/2Dfinal_%s.pkl'%(circuit_n,mechanism,filename(mechanism,parIDss)), 'wb'))
    pickle.dump(U_record, open(modellingpath + '/growth/out/numerical/%s/%s/simulation/2Drecord_%s.pkl'%(circuit_n,mechanism,filename(mechanism,parIDss)), 'wb'))
    plot1D(U_final)
    plt.show()
    mechanism = 'nogrowth'
    U_final,U_record, U0, x_grid, reduced_t_grid= cn_nogrowth(par_dict,L,J,T,N, circuit_n, tqdm_disable=True)
    pickle.dump(U_final, open(modellingpath + '/growth/out/numerical/%s/%s/simulation/2Dfinal_%s.pkl'%(circuit_n,mechanism,filename(mechanism,parIDss)), 'wb'))
    pickle.dump(U_record, open(modellingpath + '/growth/out/numerical/%s/%s/simulation/2Drecord_%s.pkl'%(circuit_n,mechanism,filename(mechanism,parIDss)), 'wb'))
    surfpattern(U_record, [x_grid, reduced_t_grid], 'linear',morphogen=0, rate=0, savefig=False,filename='',logResults=False,normalize=False)
    plt.show()
    plot1D(U_final)
    plt.show()
# #run
# no_numba = False
# if no_numba == True:
#     st = time.time()
#     U,U_record, U0, x_grid, reduced_t_grid, cellMatrix= cn_edgegrowth2(par_dict,L,J,T,N, circuit_n, rate=0.1, boundaryCoeff=2)
#     elapsed_time = time.time() - st
#     print('Execution time numba:', time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
#     plt.scatter(x_grid,cellMatrix)
#     plt.show()
#     #plot
#     plot1D(U, savefig=False,filename='')
#     plt.show()
#     surfpattern(U_record, [x_grid, reduced_t_grid], 'linear',morphogen=0, rate=0, savefig=False,filename='',logResults=False,normalize=False)
#     plt.show()
#     surfpattern(U_record, [x_grid, reduced_t_grid], 'linear',  morphogen=1, rate=0, savefig=False,filename='',logResults=False,normalize=False)
#     plt.show()
# numba=False
# if numba == True: 
#     st = time.time()
#     U,U_record, U0, x_grid, reduced_t_grid, cellMatrix= cn_edgegrowth2_numba(par_dict,L,J,T,N, circuit_n, rate=0.1, boundaryCoeff=2)
#     elapsed_time = time.time() - st
#     print('Execution time:', time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
# no_growth = True
# if no_growth == True:
#     st = time.time()
#     U,U_record, U0, x_grid, reduced_t_grid= cn_nogrowth(par_dict,L,J,T,N, circuit_n, tqdm_disable=False)
#     elapsed_time = time.time() - st
#     print('Execution time:', time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
#     #plot
#     plot1D(U, savefig=False,filename='')
#     plt.show()
#     surfpattern(U_record, [x_grid, reduced_t_grid], 'linear',morphogen=0, rate=0, savefig=False,filename='',logResults=False,normalize=False)
#     plt.show()
#     surfpattern(U_record, [x_grid, reduced_t_grid], 'linear',  morphogen=1, rate=0, savefig=False,filename='',logResults=False,normalize=False)
#     plt.show()
# print(np.sum(U_final[0]))

  0%|          | 0/31 [00:00<?, ?it/s]
31
118941.0
{'ba': 0.01, 'bb': 0.01, 'Va': 815.2281990625947, 'Vb': 869.092720545832, 'kaa': 12.891931764200102, 'kba': 42.952283653646234, 'kab': 1.6989749233704032, 'kbb': 184.85059192663672, 'mua': 1.2913151609791507, 'mub': 1.3882435185207276, 'd_B': 9.871792232811737, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([ 0.36781896, 28.03584002]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.5281387272214166+0j), 'complex_dispersion': True}
negative

  3%|▎         | 1/31 [00:04<02:02,  4.10s/it]
130517.2
{'ba': 0.01, 'bb': 0.01, 'Va': 114.2943824225333, 'Vb': 135.5191790082118, 'kaa': 31.039471979008834, 'kba': 8.382989354562715, 'kab': 23.895203185186315, 'kbb': 0.680626361413112, 'mua': 0.10629343462009974, 'mub': 0.09833370115985028, 'd_B': 8.061394127036298, 'd_A': 1.0, 'n': 2.0, 'ss_n': 4.0, 'ss_list': array([1.99812735, 9.67041749]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.002129146044943686+0j), 'complex_dispersion': True}
negative

  6%|▋         | 2/31 [00:07<01:56,  4.02s/it]
135128.1
{'ba': 0.01, 'bb': 0.01, 'Va': 27.564908966425694, 'Vb': 326.25772039061815, 'kaa': 1.7568405193731178, 'kba': 1.4857913308384587, 'kab': 3.6312436959757273, 'kbb': 58.459387568413824, 'mua': 1.5994924753282964, 'mub': 1.819611424644346, 'd_B': 8.272981979622703, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([0.29640735, 1.19224465]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.23246594566876855+0j), 'complex_dispersion': True}
negative

 10%|▉         | 3/31 [00:11<01:51,  3.96s/it]
350981.4
{'ba': 0.01, 'bb': 0.01, 'Va': 239.16045466671721, 'Vb': 34.63637728615317, 'kaa': 36.2339505371141, 'kba': 64.48723450348534, 'kab': 2.2029491724002734, 'kbb': 3.8503227579228367, 'mua': 0.176897911154491, 'mub': 0.21967063862747171, 'd_B': 9.765606067430125, 'd_A': 1.0, 'n': 2.0, 'ss_n': 5.0, 'ss_list': array([ 1.51834334, 50.82511564]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.06622513286767573+0j), 'complex_dispersion': True}
negative

 13%|█▎        | 4/31 [00:15<01:45,  3.90s/it]
351953.1
{'ba': 0.01, 'bb': 0.01, 'Va': 54.18484104015259, 'Vb': 47.25741550983462, 'kaa': 11.605058993398924, 'kba': 33.85514282492236, 'kab': 4.32564142907592, 'kbb': 57.999664182403706, 'mua': 0.408073564739178, 'mub': 0.4027145677064975, 'd_B': 8.87692980159737, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([ 3.98772768, 53.936696  ]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.028127901418268264+0j), 'complex_dispersion': True}
negative

 16%|█▌        | 5/31 [00:19<01:41,  3.91s/it]

360416.1
{'ba': 0.01, 'bb': 0.01, 'Va': 385.5088897168174, 'Vb': 110.22121523587622, 'kaa': 2.483706697966257, 'kba': 0.9711051957031617, 'kab': 0.40104277427424595, 'kbb': 42.103036140718736, 'mua': 5.250474461283016, 'mub': 14.083381020752846, 'd_B': 7.772167659674627, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([0.12386348, 0.68225491]), 'ss_class': 'stable point', 'system_class': 'turing I', 'maxeig': (0.17701025304920037+0j), 'complex_dispersion': False}

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
~/Documents/modelling/growth/src/numerical/edgegrowth2/run_numerical_multiple.py in <module>
     47     par_dict = df.loc[(parID,ss)].to_dict()
     48     print(par_dict)
---> 49     U_final,U_record, U0, x_grid, reduced_t_grid, cellMatrix= cn_edgegrowth2_numba(par_dict,L,J,T,N, circuit_n, rate=rate, boundaryCoeff=boundaryCoeff, tqdm_disable=True)
     50     pickle.dump(U_final, open(modellingpath + '/growth/out/numerical/%s/%s/simulation/2Dfinal_%s.pkl'%(circuit_n,mechanism,filename(mechanism,parIDss)), 'wb'))
     51     pickle.dump(U_record, open(modellingpath + '/growth/out/numerical/%s/%s/simulation/2Drecord_%s.pkl'%(circuit_n,mechanism,filename(mechanism,parIDss)), 'wb'))

~/Documents/modelling/lib/numerical/cn_edgegrowth2_numba.py in cn_edgegrowth2(par_dict, L, J, T, N, circuit_n, steadystate, growth, rate, n_species, perturbation, boundaryCoeff, tqdm_disable)
     61         return cellMatrix
     62     initialJ=1
---> 63     cellMatrix=cellMatrixFunction(initialJ)
     64 
     65     U0 = []

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/dispatcher.py in _compile_for_args(self, *args, **kws)
    432         return_val = None
    433         try:
--> 434             return_val = self.compile(tuple(argtypes))
    435         except errors.ForceLiteralArg as e:
    436             # Received request for compiler re-entry with the list of arguments

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/dispatcher.py in compile(self, sig)
    977                 with ev.trigger_event("numba:compile", data=ev_details):
    978                     try:
--> 979                         cres = self._compiler.compile(args, return_type)
    980                     except errors.ForceLiteralArg as e:
    981                         def folded(args, kws):

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/dispatcher.py in compile(self, args, return_type)
    139 
    140     def compile(self, args, return_type):
--> 141         status, retval = self._compile_cached(args, return_type)
    142         if status:
    143             return retval

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/dispatcher.py in _compile_cached(self, args, return_type)
    153 
    154         try:
--> 155             retval = self._compile_core(args, return_type)
    156         except errors.TypingError as e:
    157             self._failed_cache[key] = e

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/dispatcher.py in _compile_core(self, args, return_type)
    166 
    167         impl = self._get_implementation(args, {})
--> 168         cres = compiler.compile_extra(self.targetdescr.typing_context,
    169                                       self.targetdescr.target_context,
    170                                       impl,

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/compiler.py in compile_extra(typingctx, targetctx, func, args, return_type, flags, locals, library, pipeline_class)
    684     pipeline = pipeline_class(typingctx, targetctx, library,
    685                               args, return_type, flags, locals)
--> 686     return pipeline.compile_extra(func)
    687 
    688 

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/compiler.py in compile_extra(self, func)
    426         self.state.lifted = ()
    427         self.state.lifted_from = None
--> 428         return self._compile_bytecode()
    429 
    430     def compile_ir(self, func_ir, lifted=(), lifted_from=None):

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/compiler.py in _compile_bytecode(self)
    490         """
    491         assert self.state.func_ir is None
--> 492         return self._compile_core()
    493 
    494     def _compile_ir(self):

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/compiler.py in _compile_core(self)
    460                 res = None
    461                 try:
--> 462                     pm.run(self.state)
    463                     if self.state.cr is not None:
    464                         break

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/compiler_machinery.py in run(self, state)
    332                 pass_inst = _pass_registry.get(pss).pass_inst
    333                 if isinstance(pass_inst, CompilerPass):
--> 334                     self._runPass(idx, pass_inst, state)
    335                 else:
    336                     raise BaseException("Legacy pass in use")

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/compiler_lock.py in _acquire_compile_lock(*args, **kwargs)
     33         def _acquire_compile_lock(*args, **kwargs):
     34             with self:
---> 35                 return func(*args, **kwargs)
     36         return _acquire_compile_lock
     37 

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/compiler_machinery.py in _runPass(self, index, pss, internal_state)
    287             mutated |= check(pss.run_initialization, internal_state)
    288         with SimpleTimer() as pass_time:
--> 289             mutated |= check(pss.run_pass, internal_state)
    290         with SimpleTimer() as finalize_time:
    291             mutated |= check(pss.run_finalizer, internal_state)

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/compiler_machinery.py in check(func, compiler_state)
    260 
    261         def check(func, compiler_state):
--> 262             mangled = func(compiler_state)
    263             if mangled not in (True, False):
    264                 msg = ("CompilerPass implementations should return True/False. "

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/typed_passes.py in run_pass(self, state)
    423                 # Insert native function for use by other jitted-functions.
    424                 # We also register its library to allow for inlining.
--> 425                 cfunc = targetctx.get_executable(library, fndesc, env)
    426                 targetctx.insert_user_function(cfunc, fndesc, [library])
    427                 state['cr'] = _LowerResult(fndesc, call_helper,

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/cpu.py in get_executable(self, library, fndesc, env)
    228         """
    229         # Code generation
--> 230         baseptr = library.get_pointer_to_function(fndesc.llvm_func_name)
    231         fnptr = library.get_pointer_to_function(fndesc.llvm_cpython_wrapper_name)
    232 

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/codegen.py in get_pointer_to_function(self, name)
    986             - non-zero if the symbol is defined.
    987         """
--> 988         self._ensure_finalized()
    989         ee = self._codegen._engine
    990         if not ee.is_symbol_defined(name):

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/codegen.py in _ensure_finalized(self)
    566     def _ensure_finalized(self):
    567         if not self._finalized:
--> 568             self.finalize()
    569 
    570     def create_ir_module(self, name):

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/codegen.py in finalize(self)
    761         # Optimize the module after all dependences are linked in above,
    762         # to allow for inlining.
--> 763         self._optimize_final_module()
    764 
    765         self._final_module.verify()

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/codegen.py in _optimize_final_module(self)
    681         with self._recorded_timings.record(full_name):
    682             # The full optimisation suite is then run on the refop pruned IR
--> 683             self._codegen._mpm_full.run(self._final_module)
    684 
    685     def _get_module_for_linking(self):

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/llvmlite/binding/passmanagers.py in run(self, module)
    205         Run optimization passes on the given module.
    206         """
--> 207         return ffi.lib.LLVMPY_RunPassManager(self, module)
    208 
    209 

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/llvmlite/binding/ffi.py in __call__(self, *args, **kwargs)
    149     def __call__(self, *args, **kwargs):
    150         with self._lock:
--> 151             return self._cfn(*args, **kwargs)
    152 
    153 

KeyboardInterrupt:
In [ ]:
import sys
import os
from importlib_metadata import distribution
pwd = os.getcwd()
modellingpath = pwd.rpartition("modelling")[0] + pwd.rpartition("modelling")[1] 
sys.path.append(modellingpath + '/lib')
#############

from numerical.cn_edgegrowth2_numba import cn_edgegrowth2 as cn_edgegrowth2_numba
from numerical.cn_edgegrowth2 import cn_edgegrowth2
from numerical.cn_nogrowth import cn_nogrowth
from numerical.cn_plot import plot1D, surfpattern
import pickle
import matplotlib.pyplot as plt
import time
import numpy as np
from tqdm import tqdm
#system parameters

circuit_n = 'turinghill'
variant=0 
n_param_sets = 2000000
# par_dict = {'c1':0.1, 'c2':1,'c3':0.9,'c4':1, 'd_A': 1, 'd_B':10}

df= pickle.load( open(modellingpath + "/growth/out/analytical/turing/turing_df_%s_variant%r_%rparametersets.pkl"%(circuit_n,variant,n_param_sets), "rb"))
# df= pickle.load( open(modellingpath + "/growth/out/analytical/instability/instability_df_%s_variant%r_%rparametersets.pkl"%(circuit_n,variant,n_param_sets), "rb"))

df.index.names = ['parID','ss']
# df = multiple_df.xs(0, level=1)
#solver parameters




L=50; x_gridpoints=1; J=L*x_gridpoints;I=J 


T=3000; t_gridpoints = 5; N=T*t_gridpoints #Number of timepoints <below 3 is bad if x_gridpoints=1

boundaryCoeff=2;rate=0.01
print(len(df))
filename= lambda mechanism, parID: 'circuit%s_variant%s_bc%s_%s_rate%s_ID%s_L%r_J%r_T%r_N%r'%(circuit_n,variant,boundaryCoeff, mechanism,rate,parID,L,J,T,N)
for parID,ss in tqdm(df.index):
    parIDss = f'{parID}.{ss}'
    print(parIDss)
    mechanism = 'edgegrowth2'
    par_dict = df.loc[(parID,ss)].to_dict()
    print(par_dict)
    U_final,U_record, U0, x_grid, reduced_t_grid, cellMatrix= cn_edgegrowth2_numba(par_dict,L,J,T,N, circuit_n, rate=rate, boundaryCoeff=boundaryCoeff, tqdm_disable=True)
    surfpattern(U_record, [x_grid, reduced_t_grid], 'linear',morphogen=0, rate=0, savefig=False,filename='',logResults=False,normalize=False)
    plt.show()
    pickle.dump(U_final, open(modellingpath + '/growth/out/numerical/%s/%s/simulation/2Dfinal_%s.pkl'%(circuit_n,mechanism,filename(mechanism,parIDss)), 'wb'))
    pickle.dump(U_record, open(modellingpath + '/growth/out/numerical/%s/%s/simulation/2Drecord_%s.pkl'%(circuit_n,mechanism,filename(mechanism,parIDss)), 'wb'))
    plot1D(U_final)
    plt.show()
    mechanism = 'nogrowth'
    U_final,U_record, U0, x_grid, reduced_t_grid= cn_nogrowth(par_dict,L,J,T,N, circuit_n, tqdm_disable=True)
    pickle.dump(U_final, open(modellingpath + '/growth/out/numerical/%s/%s/simulation/2Dfinal_%s.pkl'%(circuit_n,mechanism,filename(mechanism,parIDss)), 'wb'))
    pickle.dump(U_record, open(modellingpath + '/growth/out/numerical/%s/%s/simulation/2Drecord_%s.pkl'%(circuit_n,mechanism,filename(mechanism,parIDss)), 'wb'))
    surfpattern(U_record, [x_grid, reduced_t_grid], 'linear',morphogen=0, rate=0, savefig=False,filename='',logResults=False,normalize=False)
    plt.show()
    plot1D(U_final)
    plt.show()
# #run
# no_numba = False
# if no_numba == True:
#     st = time.time()
#     U,U_record, U0, x_grid, reduced_t_grid, cellMatrix= cn_edgegrowth2(par_dict,L,J,T,N, circuit_n, rate=0.1, boundaryCoeff=2)
#     elapsed_time = time.time() - st
#     print('Execution time numba:', time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
#     plt.scatter(x_grid,cellMatrix)
#     plt.show()
#     #plot
#     plot1D(U, savefig=False,filename='')
#     plt.show()
#     surfpattern(U_record, [x_grid, reduced_t_grid], 'linear',morphogen=0, rate=0, savefig=False,filename='',logResults=False,normalize=False)
#     plt.show()
#     surfpattern(U_record, [x_grid, reduced_t_grid], 'linear',  morphogen=1, rate=0, savefig=False,filename='',logResults=False,normalize=False)
#     plt.show()
# numba=False
# if numba == True: 
#     st = time.time()
#     U,U_record, U0, x_grid, reduced_t_grid, cellMatrix= cn_edgegrowth2_numba(par_dict,L,J,T,N, circuit_n, rate=0.1, boundaryCoeff=2)
#     elapsed_time = time.time() - st
#     print('Execution time:', time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
# no_growth = True
# if no_growth == True:
#     st = time.time()
#     U,U_record, U0, x_grid, reduced_t_grid= cn_nogrowth(par_dict,L,J,T,N, circuit_n, tqdm_disable=False)
#     elapsed_time = time.time() - st
#     print('Execution time:', time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
#     #plot
#     plot1D(U, savefig=False,filename='')
#     plt.show()
#     surfpattern(U_record, [x_grid, reduced_t_grid], 'linear',morphogen=0, rate=0, savefig=False,filename='',logResults=False,normalize=False)
#     plt.show()
#     surfpattern(U_record, [x_grid, reduced_t_grid], 'linear',  morphogen=1, rate=0, savefig=False,filename='',logResults=False,normalize=False)
#     plt.show()
# print(np.sum(U_final[0]))

  0%|          | 0/31 [00:00<?, ?it/s]
31
118941.0
{'ba': 0.01, 'bb': 0.01, 'Va': 815.2281990625947, 'Vb': 869.092720545832, 'kaa': 12.891931764200102, 'kba': 42.952283653646234, 'kab': 1.6989749233704032, 'kbb': 184.85059192663672, 'mua': 1.2913151609791507, 'mub': 1.3882435185207276, 'd_B': 9.871792232811737, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([ 0.36781896, 28.03584002]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.5281387272214166+0j), 'complex_dispersion': True}
negative

  3%|▎         | 1/31 [00:04<02:04,  4.14s/it]
130517.2
{'ba': 0.01, 'bb': 0.01, 'Va': 114.2943824225333, 'Vb': 135.5191790082118, 'kaa': 31.039471979008834, 'kba': 8.382989354562715, 'kab': 23.895203185186315, 'kbb': 0.680626361413112, 'mua': 0.10629343462009974, 'mub': 0.09833370115985028, 'd_B': 8.061394127036298, 'd_A': 1.0, 'n': 2.0, 'ss_n': 4.0, 'ss_list': array([1.99812735, 9.67041749]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.002129146044943686+0j), 'complex_dispersion': True}
negative

  6%|▋         | 2/31 [00:08<02:01,  4.19s/it]
135128.1
{'ba': 0.01, 'bb': 0.01, 'Va': 27.564908966425694, 'Vb': 326.25772039061815, 'kaa': 1.7568405193731178, 'kba': 1.4857913308384587, 'kab': 3.6312436959757273, 'kbb': 58.459387568413824, 'mua': 1.5994924753282964, 'mub': 1.819611424644346, 'd_B': 8.272981979622703, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([0.29640735, 1.19224465]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.23246594566876855+0j), 'complex_dispersion': True}
negative

 10%|▉         | 3/31 [00:12<01:55,  4.11s/it]
350981.4
{'ba': 0.01, 'bb': 0.01, 'Va': 239.16045466671721, 'Vb': 34.63637728615317, 'kaa': 36.2339505371141, 'kba': 64.48723450348534, 'kab': 2.2029491724002734, 'kbb': 3.8503227579228367, 'mua': 0.176897911154491, 'mub': 0.21967063862747171, 'd_B': 9.765606067430125, 'd_A': 1.0, 'n': 2.0, 'ss_n': 5.0, 'ss_list': array([ 1.51834334, 50.82511564]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.06622513286767573+0j), 'complex_dispersion': True}
negative

 13%|█▎        | 4/31 [00:16<01:49,  4.07s/it]
351953.1
{'ba': 0.01, 'bb': 0.01, 'Va': 54.18484104015259, 'Vb': 47.25741550983462, 'kaa': 11.605058993398924, 'kba': 33.85514282492236, 'kab': 4.32564142907592, 'kbb': 57.999664182403706, 'mua': 0.408073564739178, 'mub': 0.4027145677064975, 'd_B': 8.87692980159737, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([ 3.98772768, 53.936696  ]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.028127901418268264+0j), 'complex_dispersion': True}
negative

 16%|█▌        | 5/31 [00:20<01:46,  4.11s/it]
360416.1
{'ba': 0.01, 'bb': 0.01, 'Va': 385.5088897168174, 'Vb': 110.22121523587622, 'kaa': 2.483706697966257, 'kba': 0.9711051957031617, 'kab': 0.40104277427424595, 'kbb': 42.103036140718736, 'mua': 5.250474461283016, 'mub': 14.083381020752846, 'd_B': 7.772167659674627, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([0.12386348, 0.68225491]), 'ss_class': 'stable point', 'system_class': 'turing I', 'maxeig': (0.17701025304920037+0j), 'complex_dispersion': False}
negative
negative
negative

 19%|█▉        | 6/31 [00:24<01:41,  4.07s/it]
472883.2
{'ba': 0.01, 'bb': 0.01, 'Va': 290.6327418040253, 'Vb': 11.45621109841897, 'kaa': 14.439605207063384, 'kba': 0.7407539897652289, 'kab': 8.226865404352063, 'kbb': 160.62343879687893, 'mua': 3.3465368714702333, 'mub': 3.9901290059736207, 'd_B': 6.620820479626943, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([3.94695606, 0.5397153 ]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.5550199052762483+0j), 'complex_dispersion': True}

 23%|██▎       | 7/31 [00:28<01:36,  4.04s/it]
486068.2
{'ba': 0.01, 'bb': 0.01, 'Va': 997.2903282608314, 'Vb': 43.111126294326105, 'kaa': 155.8052786664667, 'kba': 5.559437646546131, 'kab': 17.475798599213125, 'kbb': 0.11000134341052847, 'mua': 0.048235757764698135, 'mub': 0.0802045896337645, 'd_B': 7.693765988716828, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([1.36567456, 3.38727262]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.0021226246628133216+0j), 'complex_dispersion': True}
negative

 26%|██▌       | 8/31 [00:32<01:32,  4.04s/it]
546109.1
{'ba': 0.01, 'bb': 0.01, 'Va': 202.03958750174837, 'Vb': 34.49501460261308, 'kaa': 3.375462276950734, 'kba': 20.057786219077514, 'kab': 0.4415101363086828, 'kbb': 37.15703655950709, 'mua': 0.18011977945255836, 'mub': 0.14115099075969806, 'd_B': 5.773968725763372, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([  0.59267954, 157.23763183]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.013721466808561122+0j), 'complex_dispersion': True}

 29%|██▉       | 9/31 [00:36<01:28,  4.04s/it]
742006.2
{'ba': 0.01, 'bb': 0.01, 'Va': 115.40492671282917, 'Vb': 35.75298038735518, 'kaa': 2.3843297731692674, 'kba': 3.9720414027865862, 'kab': 0.22270580063721748, 'kbb': 2.4367359875485, 'mua': 1.1586282735305444, 'mub': 1.2757264967083521, 'd_B': 5.211852873994694, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([0.0676013 , 2.37223273]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.16226784711775366+0j), 'complex_dispersion': True}

 32%|███▏      | 10/31 [00:40<01:24,  4.01s/it]
862004.2
{'ba': 0.01, 'bb': 0.01, 'Va': 15.342934792094486, 'Vb': 754.2975569594662, 'kaa': 1.6557933391612487, 'kba': 8.439693370086474, 'kab': 6.603317355743912, 'kbb': 8.006935541273098, 'mua': 2.7471878890929213, 'mub': 2.1766960056095455, 'd_B': 6.436259387327192, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([0.74958926, 4.41325274]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.4247302804824109+0j), 'complex_dispersion': True}

 35%|███▌      | 11/31 [00:44<01:19,  3.99s/it]
1121752.2
{'ba': 0.01, 'bb': 0.01, 'Va': 35.09596281919261, 'Vb': 42.009523487375624, 'kaa': 204.67025945320665, 'kba': 129.52996179175673, 'kab': 196.2069645904714, 'kbb': 0.2434143443237951, 'mua': 0.02061094760029868, 'mub': 0.020158654231861257, 'd_B': 7.5919466938069515, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([ 49.09562577, 123.28756485]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.0008073348759318894+0j), 'complex_dispersion': True}
negative

 39%|███▊      | 12/31 [00:48<01:15,  3.99s/it]
1133916.0
{'ba': 0.01, 'bb': 0.01, 'Va': 25.28034579814929, 'Vb': 24.820602749573567, 'kaa': 0.46665031483665004, 'kba': 2.894328309523785, 'kab': 0.17150018214289278, 'kbb': 22.58855246886253, 'mua': 2.3401280586314592, 'mub': 1.8123012418852684, 'd_B': 9.29657417053512, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([0.20345611, 8.01213494]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.0951403612171493+0j), 'complex_dispersion': True}
negative

 42%|████▏     | 13/31 [00:52<01:11,  3.95s/it]
1195022.4
{'ba': 0.01, 'bb': 0.01, 'Va': 611.4433330118857, 'Vb': 65.41842217773055, 'kaa': 3.679403701088877, 'kba': 4.9161447235193645, 'kab': 0.2918676371684259, 'kbb': 23.16548966879861, 'mua': 2.681935721389821, 'mub': 3.6395321051690397, 'd_B': 3.8688919677210523, 'd_A': 1.0, 'n': 2.0, 'ss_n': 5.0, 'ss_list': array([ 0.40738494, 11.88045562]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.143157693219905+0j), 'complex_dispersion': True}

 45%|████▌     | 14/31 [00:56<01:07,  3.96s/it]
1251569.4
{'ba': 0.01, 'bb': 0.01, 'Va': 78.3363322679065, 'Vb': 335.81879962648486, 'kaa': 4.829453494045339, 'kba': 130.86352922113667, 'kab': 0.6996888224659922, 'kbb': 126.26882930476437, 'mua': 0.49939894255688555, 'mub': 0.6586702772927231, 'd_B': 8.65380960982406, 'd_A': 1.0, 'n': 2.0, 'ss_n': 5.0, 'ss_list': array([  0.68858169, 250.85811902]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.04685998948372605+0j), 'complex_dispersion': True}
negative

 48%|████▊     | 15/31 [01:00<01:03,  3.96s/it]
1271426.4
{'ba': 0.01, 'bb': 0.01, 'Va': 190.22562841368324, 'Vb': 293.57445146053476, 'kaa': 6.800965172133764, 'kba': 0.2207350784378043, 'kab': 246.46224222918642, 'kbb': 0.3525542163110288, 'mua': 0.05496767705865642, 'mub': 0.006625831542775947, 'd_B': 7.974703275984807, 'd_A': 1.0, 'n': 2.0, 'ss_n': 6.0, 'ss_list': array([0.41344634, 1.62368288]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.0014508345841911796+0j), 'complex_dispersion': True}
negative

 52%|█████▏    | 16/31 [01:04<00:59,  4.00s/it]
1282460.3
{'ba': 0.01, 'bb': 0.01, 'Va': 137.40868705731026, 'Vb': 46.23022959713599, 'kaa': 4.385335909142999, 'kba': 22.875973534242117, 'kab': 0.2523078622811597, 'kbb': 0.7088325932748427, 'mua': 0.6043877303369629, 'mub': 0.5952921481552054, 'd_B': 7.041691131306341, 'd_A': 1.0, 'n': 2.0, 'ss_n': 6.0, 'ss_list': array([ 0.18338943, 26.86238075]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.05459776834667229+0j), 'complex_dispersion': True}

 55%|█████▍    | 17/31 [01:08<00:56,  4.05s/it]
1363184.1
{'ba': 0.01, 'bb': 0.01, 'Va': 489.8671005258825, 'Vb': 792.6915956318819, 'kaa': 23.23039953009945, 'kba': 8.414381315042213, 'kab': 44.27483619151162, 'kbb': 60.43230607871443, 'mua': 4.384668940225246, 'mub': 4.435087240485868, 'd_B': 8.081678478090167, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([8.33716782, 6.12283299]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.3926141426465968+0j), 'complex_dispersion': True}
negative

 58%|█████▊    | 18/31 [01:12<00:52,  4.02s/it]
1365378.2
{'ba': 0.01, 'bb': 0.01, 'Va': 47.893378801210034, 'Vb': 303.5862850568044, 'kaa': 12.846916651306918, 'kba': 59.29856210446242, 'kab': 4.40650368007774, 'kbb': 2.4980566368646926, 'mua': 0.17139154270706858, 'mub': 0.2410507179492761, 'd_B': 4.899399859365768, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([ 0.79077203, 39.33513345]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.0023076793524887673+0j), 'complex_dispersion': True}

 61%|██████▏   | 19/31 [01:16<00:49,  4.15s/it]
1511110.2
{'ba': 0.01, 'bb': 0.01, 'Va': 197.01617697598112, 'Vb': 14.839225143838895, 'kaa': 21.290596844116017, 'kba': 70.58163906952495, 'kab': 1.5274893270274876, 'kbb': 213.0615544604696, 'mua': 0.02646202856244844, 'mub': 0.022356226626566965, 'd_B': 6.5775044835814525, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([  2.45236646, 478.67726623]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.0031422137425548696+0j), 'complex_dispersion': True}

 65%|██████▍   | 20/31 [01:20<00:45,  4.12s/it]
1591223.2
{'ba': 0.01, 'bb': 0.01, 'Va': 181.44888622132726, 'Vb': 124.23883449762988, 'kaa': 1.9077517466192115, 'kba': 0.9491899474542316, 'kab': 1.236091786253191, 'kbb': 0.7330658560779242, 'mua': 8.468121211484974, 'mub': 8.174363578560122, 'd_B': 4.305125014827109, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([0.28267662, 0.75656438]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.281755258396565+0j), 'complex_dispersion': True}

 68%|██████▊   | 21/31 [01:24<00:40,  4.09s/it]
1690913.2
{'ba': 0.01, 'bb': 0.01, 'Va': 352.7320005286167, 'Vb': 181.5972063730003, 'kaa': 22.845293171242496, 'kba': 169.51933721889947, 'kab': 1.3244300604518457, 'kbb': 0.3287189111431211, 'mua': 0.2841278375986933, 'mub': 0.2722956078114015, 'd_B': 9.110463685725591, 'd_A': 1.0, 'n': 2.0, 'ss_n': 5.0, 'ss_list': array([  0.71352878, 150.06077262]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.0850329728032928+0j), 'complex_dispersion': True}
negative

 71%|███████   | 22/31 [01:28<00:36,  4.04s/it]
1695603.2
{'ba': 0.01, 'bb': 0.01, 'Va': 31.97744639010365, 'Vb': 355.74700225932463, 'kaa': 0.5949376437041896, 'kba': 0.1548002535733322, 'kab': 3.0446652198761677, 'kbb': 0.19553009847235556, 'mua': 7.723634366609317, 'mub': 7.413257117473428, 'd_B': 8.454430875350083, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([0.16170534, 0.13633008]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.6959259882071223+0j), 'complex_dispersion': True}
negative

 74%|███████▍  | 23/31 [01:32<00:32,  4.06s/it]
1706579.2
{'ba': 0.01, 'bb': 0.01, 'Va': 59.65913434905526, 'Vb': 58.37513321525424, 'kaa': 19.49110411915976, 'kba': 58.35774236702684, 'kab': 3.4847416149037334, 'kbb': 203.24584674616693, 'mua': 0.08457531491226752, 'mub': 0.07481531387993656, 'd_B': 9.425369788961964, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([ 1.02808973, 62.60937462]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.0047861200244455615+0j), 'complex_dispersion': True}
negative

 77%|███████▋  | 24/31 [01:37<00:29,  4.16s/it]
1710204.2
{'ba': 0.01, 'bb': 0.01, 'Va': 216.47211636902944, 'Vb': 83.96995474560133, 'kaa': 13.679493144396407, 'kba': 3.4999455377566093, 'kab': 12.575575628343543, 'kbb': 14.762696556376671, 'mua': 3.986566405021081, 'mub': 6.215158423693362, 'd_B': 7.634227219284631, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([5.23818164, 1.99913804]), 'ss_class': 'stable point', 'system_class': 'turing I', 'maxeig': (0.5528654870556391+0j), 'complex_dispersion': False}
negative

 81%|████████  | 25/31 [01:41<00:25,  4.17s/it]
1808247.2
{'ba': 0.01, 'bb': 0.01, 'Va': 115.56024514084763, 'Vb': 388.29979364710476, 'kaa': 6.223902031641141, 'kba': 2.5766935598568312, 'kab': 12.162629164481306, 'kbb': 66.47320647479549, 'mua': 2.598519397485629, 'mub': 2.727407326346441, 'd_B': 6.89360915836798, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([1.41924552, 1.91617636]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.3801366210914452+0j), 'complex_dispersion': True}

 84%|████████▍ | 26/31 [01:45<00:21,  4.23s/it]
1809921.2
{'ba': 0.01, 'bb': 0.01, 'Va': 63.3691018173114, 'Vb': 364.64271636876924, 'kaa': 3.141309596623848, 'kba': 138.10754774972156, 'kab': 0.5698650306238873, 'kbb': 1.727839717663489, 'mua': 0.6103431019026028, 'mub': 0.6230432044735008, 'd_B': 6.612335350387968, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([  0.64775478, 329.93235774]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.05649640258731825+0j), 'complex_dispersion': True}

 87%|████████▋ | 27/31 [01:50<00:16,  4.23s/it]
1814923.2
{'ba': 0.01, 'bb': 0.01, 'Va': 53.56055927437798, 'Vb': 13.54273573117521, 'kaa': 2.373041388976948, 'kba': 1.1604487885770782, 'kab': 0.8192073670062823, 'kbb': 1.5034720703746312, 'mua': 2.4032596628365006, 'mub': 2.9870937016665025, 'd_B': 7.673754531362256, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([0.38006234, 0.80634261]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.7280681395694426+0j), 'complex_dispersion': True}
negative

 90%|█████████ | 28/31 [01:54<00:12,  4.15s/it]
1826994.2
{'ba': 0.01, 'bb': 0.01, 'Va': 41.291370463081314, 'Vb': 10.931651652967993, 'kaa': 8.881407254967614, 'kba': 0.5957948694497539, 'kab': 18.624381839430516, 'kbb': 0.23540646060770318, 'mua': 1.2075381545788222, 'mub': 1.2521407267897737, 'd_B': 8.378671879819821, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([4.05409146, 0.40294386]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.018427239301447518+0j), 'complex_dispersion': True}
negative

 94%|█████████▎| 29/31 [01:58<00:08,  4.18s/it]
1869288.0
{'ba': 0.01, 'bb': 0.01, 'Va': 727.246993009074, 'Vb': 34.30308217612897, 'kaa': 4.047834669345688, 'kba': 1.4697684317594815, 'kab': 0.2123372822054281, 'kbb': 1.2004593182769858, 'mua': 1.2932608263221428, 'mub': 0.8170943519827769, 'd_B': 9.67518351441563, 'd_A': 1.0, 'n': 2.0, 'ss_n': 1.0, 'ss_list': array([0.03357005, 1.03598035]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.1400827771050075+0j), 'complex_dispersion': True}
negative

 97%|█████████▋| 30/31 [02:02<00:04,  4.20s/it]
1874504.1
{'ba': 0.01, 'bb': 0.01, 'Va': 122.94933403153534, 'Vb': 29.388223066040922, 'kaa': 27.486707820855447, 'kba': 26.80106743743048, 'kab': 4.632377342389826, 'kbb': 5.63766610052431, 'mua': 0.21246309976977518, 'mub': 0.272714419860516, 'd_B': 9.238315564038194, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([ 2.79687796, 28.82539555]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.037788252039048675+0j), 'complex_dispersion': True}
negative

100%|██████████| 31/31 [02:06<00:00,  4.09s/it]
In [ ]:
import sys
import os
from importlib_metadata import distribution
pwd = os.getcwd()
modellingpath = pwd.rpartition("modelling")[0] + pwd.rpartition("modelling")[1] 
sys.path.append(modellingpath + '/lib')
#############

from numerical.cn_edgegrowth2_numba import cn_edgegrowth2 as cn_edgegrowth2_numba
from numerical.cn_edgegrowth2 import cn_edgegrowth2
from numerical.cn_nogrowth import cn_nogrowth
from numerical.cn_plot import plot1D, surfpattern
import pickle
import matplotlib.pyplot as plt
import time
import numpy as np
from tqdm import tqdm
#system parameters

circuit_n = 'turinghill'
variant=0 
n_param_sets = 2000000
# par_dict = {'c1':0.1, 'c2':1,'c3':0.9,'c4':1, 'd_A': 1, 'd_B':10}

df= pickle.load( open(modellingpath + "/growth/out/analytical/turing/turing_df_%s_variant%r_%rparametersets.pkl"%(circuit_n,variant,n_param_sets), "rb"))
# df= pickle.load( open(modellingpath + "/growth/out/analytical/instability/instability_df_%s_variant%r_%rparametersets.pkl"%(circuit_n,variant,n_param_sets), "rb"))

df.index.names = ['parID','ss']
# df = multiple_df.xs(0, level=1)
#solver parameters




L=50; x_gridpoints=1; J=L*x_gridpoints;I=J 


T=3000; t_gridpoints = 5; N=T*t_gridpoints #Number of timepoints <below 3 is bad if x_gridpoints=1

boundaryCoeff=2;rate=0.01
print(len(df))
filename= lambda mechanism, parID: 'circuit%s_variant%s_bc%s_%s_rate%s_ID%s_L%r_J%r_T%r_N%r'%(circuit_n,variant,boundaryCoeff, mechanism,rate,parID,L,J,T,N)
for parID,ss in tqdm(df.index):
    parIDss = f'{parID}.{ss}'
    print(parIDss)
    mechanism = 'edgegrowth2'
    par_dict = df.loc[(parID,ss)].to_dict()
    print(par_dict)
    U_final,U_record, U0, x_grid, reduced_t_grid, cellMatrix= cn_edgegrowth2_numba(par_dict,L,J,T,N, circuit_n, rate=rate, boundaryCoeff=boundaryCoeff, tqdm_disable=True)
    plt.scatter(x_grid,cellMatrix)
    plt.show()
    surfpattern(U_record, [x_grid, reduced_t_grid], 'linear',morphogen=0, rate=0, savefig=False,filename='',logResults=False,normalize=False)
    plt.show()
    pickle.dump(U_final, open(modellingpath + '/growth/out/numerical/%s/%s/simulation/2Dfinal_%s.pkl'%(circuit_n,mechanism,filename(mechanism,parIDss)), 'wb'))
    pickle.dump(U_record, open(modellingpath + '/growth/out/numerical/%s/%s/simulation/2Drecord_%s.pkl'%(circuit_n,mechanism,filename(mechanism,parIDss)), 'wb'))
    plot1D(U_final)
    plt.show()
    mechanism = 'nogrowth'
    U_final,U_record, U0, x_grid, reduced_t_grid= cn_nogrowth(par_dict,L,J,T,N, circuit_n, tqdm_disable=True)
    pickle.dump(U_final, open(modellingpath + '/growth/out/numerical/%s/%s/simulation/2Dfinal_%s.pkl'%(circuit_n,mechanism,filename(mechanism,parIDss)), 'wb'))
    pickle.dump(U_record, open(modellingpath + '/growth/out/numerical/%s/%s/simulation/2Drecord_%s.pkl'%(circuit_n,mechanism,filename(mechanism,parIDss)), 'wb'))
    surfpattern(U_record, [x_grid, reduced_t_grid], 'linear',morphogen=0, rate=0, savefig=False,filename='',logResults=False,normalize=False)
    plt.show()
    plot1D(U_final)
    plt.show()
# #run
# no_numba = False
# if no_numba == True:
#     st = time.time()
#     U,U_record, U0, x_grid, reduced_t_grid, cellMatrix= cn_edgegrowth2(par_dict,L,J,T,N, circuit_n, rate=0.1, boundaryCoeff=2)
#     elapsed_time = time.time() - st
#     print('Execution time numba:', time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
#     plt.scatter(x_grid,cellMatrix)
#     plt.show()
#     #plot
#     plot1D(U, savefig=False,filename='')
#     plt.show()
#     surfpattern(U_record, [x_grid, reduced_t_grid], 'linear',morphogen=0, rate=0, savefig=False,filename='',logResults=False,normalize=False)
#     plt.show()
#     surfpattern(U_record, [x_grid, reduced_t_grid], 'linear',  morphogen=1, rate=0, savefig=False,filename='',logResults=False,normalize=False)
#     plt.show()
# numba=False
# if numba == True: 
#     st = time.time()
#     U,U_record, U0, x_grid, reduced_t_grid, cellMatrix= cn_edgegrowth2_numba(par_dict,L,J,T,N, circuit_n, rate=0.1, boundaryCoeff=2)
#     elapsed_time = time.time() - st
#     print('Execution time:', time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
# no_growth = True
# if no_growth == True:
#     st = time.time()
#     U,U_record, U0, x_grid, reduced_t_grid= cn_nogrowth(par_dict,L,J,T,N, circuit_n, tqdm_disable=False)
#     elapsed_time = time.time() - st
#     print('Execution time:', time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))
#     #plot
#     plot1D(U, savefig=False,filename='')
#     plt.show()
#     surfpattern(U_record, [x_grid, reduced_t_grid], 'linear',morphogen=0, rate=0, savefig=False,filename='',logResults=False,normalize=False)
#     plt.show()
#     surfpattern(U_record, [x_grid, reduced_t_grid], 'linear',  morphogen=1, rate=0, savefig=False,filename='',logResults=False,normalize=False)
#     plt.show()
# print(np.sum(U_final[0]))

  0%|          | 0/31 [00:00<?, ?it/s]
31
118941.0
{'ba': 0.01, 'bb': 0.01, 'Va': 815.2281990625947, 'Vb': 869.092720545832, 'kaa': 12.891931764200102, 'kba': 42.952283653646234, 'kab': 1.6989749233704032, 'kbb': 184.85059192663672, 'mua': 1.2913151609791507, 'mub': 1.3882435185207276, 'd_B': 9.871792232811737, 'd_A': 1.0, 'n': 2.0, 'ss_n': 3.0, 'ss_list': array([ 0.36781896, 28.03584002]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.5281387272214166+0j), 'complex_dispersion': True}
negative

  3%|▎         | 1/31 [00:04<02:03,  4.10s/it]
130517.2
{'ba': 0.01, 'bb': 0.01, 'Va': 114.2943824225333, 'Vb': 135.5191790082118, 'kaa': 31.039471979008834, 'kba': 8.382989354562715, 'kab': 23.895203185186315, 'kbb': 0.680626361413112, 'mua': 0.10629343462009974, 'mub': 0.09833370115985028, 'd_B': 8.061394127036298, 'd_A': 1.0, 'n': 2.0, 'ss_n': 4.0, 'ss_list': array([1.99812735, 9.67041749]), 'ss_class': 'stable spiral', 'system_class': 'turing I oscillatory', 'maxeig': (0.002129146044943686+0j), 'complex_dispersion': True}










    
    






---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
~/Documents/modelling/growth/src/numerical/edgegrowth2/run_numerical_multiple.py in <module>
     47     par_dict = df.loc[(parID,ss)].to_dict()
     48     print(par_dict)
---> 49     U_final,U_record, U0, x_grid, reduced_t_grid, cellMatrix= cn_edgegrowth2_numba(par_dict,L,J,T,N, circuit_n, rate=rate, boundaryCoeff=boundaryCoeff, tqdm_disable=True)
     50     plt.scatter(x_grid,cellMatrix)
     51     plt.show()

~/Documents/modelling/lib/numerical/cn_edgegrowth2_numba.py in cn_edgegrowth2(par_dict, L, J, T, N, circuit_n, steadystate, growth, rate, n_species, perturbation, boundaryCoeff, tqdm_disable)
     61         return cellMatrix
     62     initialJ=1
---> 63     cellMatrix=cellMatrixFunction(initialJ)
     64 
     65     U0 = []

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/dispatcher.py in _compile_for_args(self, *args, **kws)
    432         return_val = None
    433         try:
--> 434             return_val = self.compile(tuple(argtypes))
    435         except errors.ForceLiteralArg as e:
    436             # Received request for compiler re-entry with the list of arguments

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/dispatcher.py in compile(self, sig)
    977                 with ev.trigger_event("numba:compile", data=ev_details):
    978                     try:
--> 979                         cres = self._compiler.compile(args, return_type)
    980                     except errors.ForceLiteralArg as e:
    981                         def folded(args, kws):

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/dispatcher.py in compile(self, args, return_type)
    139 
    140     def compile(self, args, return_type):
--> 141         status, retval = self._compile_cached(args, return_type)
    142         if status:
    143             return retval

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/dispatcher.py in _compile_cached(self, args, return_type)
    153 
    154         try:
--> 155             retval = self._compile_core(args, return_type)
    156         except errors.TypingError as e:
    157             self._failed_cache[key] = e

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/dispatcher.py in _compile_core(self, args, return_type)
    166 
    167         impl = self._get_implementation(args, {})
--> 168         cres = compiler.compile_extra(self.targetdescr.typing_context,
    169                                       self.targetdescr.target_context,
    170                                       impl,

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/compiler.py in compile_extra(typingctx, targetctx, func, args, return_type, flags, locals, library, pipeline_class)
    684     pipeline = pipeline_class(typingctx, targetctx, library,
    685                               args, return_type, flags, locals)
--> 686     return pipeline.compile_extra(func)
    687 
    688 

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/compiler.py in compile_extra(self, func)
    422     def compile_extra(self, func):
    423         self.state.func_id = bytecode.FunctionIdentity.from_function(func)
--> 424         ExtractByteCode().run_pass(self.state)
    425 
    426         self.state.lifted = ()

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/untyped_passes.py in run_pass(self, state)
     62         """
     63         func_id = state['func_id']
---> 64         bc = bytecode.ByteCode(func_id)
     65         if config.DUMP_BYTECODE:
     66             print(bc.dump())

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/bytecode.py in __init__(self, func_id)
    196 
    197         # A map of {offset: ByteCodeInst}
--> 198         table = OrderedDict(ByteCodeIter(code))
    199         self._compute_lineno(table, code)
    200 

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/bytecode.py in next(self)
    168 
    169     def next(self):
--> 170         offset, opcode, arg, nextoffset = self._fetch_opcode()
    171         return offset, ByteCodeInst(offset=offset, opcode=opcode, arg=arg,
    172                                     nextoffset=nextoffset)

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/bytecode.py in _fetch_opcode(self)
    165 
    166     def _fetch_opcode(self):
--> 167         return next(self.iter)
    168 
    169     def next(self):

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/bytecode.py in _patched_opargs(bc_stream)
    149     yield (0, OPCODE_NOP, None, _FIXED_OFFSET)
    150     # Adjust bytecode offset for the rest of the stream
--> 151     for offset, opcode, arg, nextoffset in bc_stream:
    152         # If the opcode has an absolute jump target, adjust it.
    153         if opcode in JABS_OPS:

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numba/core/bytecode.py in _unpack_opargs(code)
    125         i += CODE_LEN
    126         if op >= HAVE_ARGUMENT:
--> 127             arg = code[i] | extended_arg
    128             for j in range(ARG_LEN):
    129                 arg |= code[i + j] << (8 * j)

KeyboardInterrupt: 























In [ ]:



     


df



     






















    
    
Out[ ]:











  
    

      
      
      ba
      bb
      Va
      Vb
      kaa
      kba
      kab
      kbb
      mua
      mub
      d_B
      d_A
      n
      ss_n
      ss_list
      ss_class
      system_class
      maxeig
      complex_dispersion
    

    

      parID
      ss
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      118941
      0
      0.01
      0.01
      815.228199
      869.092721
      12.891932
      42.952284
      1.698975
      184.850592
      1.291315
      1.388244
      9.871792
      1.0
      2.0
      3.0
      [0.3678189590545821, 28.035840015553944]
      stable spiral
      turing I oscillatory
      0.528139+0.000000j
      True
    

    

      130517
      2
      0.01
      0.01
      114.294382
      135.519179
      31.039472
      8.382989
      23.895203
      0.680626
      0.106293
      0.098334
      8.061394
      1.0
      2.0
      4.0
      [1.9981273546814948, 9.670417490187942]
      stable spiral
      turing I oscillatory
      0.002129+0.000000j
      True
    

    

      135128
      1
      0.01
      0.01
      27.564909
      326.25772
      1.756841
      1.485791
      3.631244
      58.459388
      1.599492
      1.819611
      8.272982
      1.0
      2.0
      3.0
      [0.2964073513214895, 1.1922446532071926]
      stable spiral
      turing I oscillatory
      0.232466+0.000000j
      True
    

    

      350981
      4
      0.01
      0.01
      239.160455
      34.636377
      36.233951
      64.487235
      2.202949
      3.850323
      0.176898
      0.219671
      9.765606
      1.0
      2.0
      5.0
      [1.5183433404147844, 50.82511564321475]
      stable spiral
      turing I oscillatory
      0.066225+0.000000j
      True
    

    

      351953
      1
      0.01
      0.01
      54.184841
      47.257416
      11.605059
      33.855143
      4.325641
      57.999664
      0.408074
      0.402715
      8.87693
      1.0
      2.0
      3.0
      [3.9877276826980332, 53.93669600164983]
      stable spiral
      turing I oscillatory
      0.028128+0.000000j
      True
    

    

      360416
      1
      0.01
      0.01
      385.50889
      110.221215
      2.483707
      0.971105
      0.401043
      42.103036
      5.250474
      14.083381
      7.772168
      1.0
      2.0
      3.0
      [0.12386347936019793, 0.6822549113494273]
      stable point
      turing I
      0.177010+0.000000j
      False
    

    

      472883
      2
      0.01
      0.01
      290.632742
      11.456211
      14.439605
      0.740754
      8.226865
      160.623439
      3.346537
      3.990129
      6.62082
      1.0
      2.0
      3.0
      [3.946956059941068, 0.539715295276271]
      stable spiral
      turing I oscillatory
      0.555020+0.000000j
      True
    

    

      486068
      2
      0.01
      0.01
      997.290328
      43.111126
      155.805279
      5.559438
      17.475799
      0.110001
      0.048236
      0.080205
      7.693766
      1.0
      2.0
      3.0
      [1.3656745565250377, 3.387272620199473]
      stable spiral
      turing I oscillatory
      0.002123+0.000000j
      True
    

    

      546109
      1
      0.01
      0.01
      202.039588
      34.495015
      3.375462
      20.057786
      0.44151
      37.157037
      0.18012
      0.141151
      5.773969
      1.0
      2.0
      3.0
      [0.5926795355799822, 157.23763183456734]
      stable spiral
      turing I oscillatory
      0.013721+0.000000j
      True
    

    

      742006
      2
      0.01
      0.01
      115.404927
      35.75298
      2.38433
      3.972041
      0.222706
      2.436736
      1.158628
      1.275726
      5.211853
      1.0
      2.0
      3.0
      [0.06760130262009373, 2.372232733681772]
      stable spiral
      turing I oscillatory
      0.162268+0.000000j
      True
    

    

      862004
      2
      0.01
      0.01
      15.342935
      754.297557
      1.655793
      8.439693
      6.603317
      8.006936
      2.747188
      2.176696
      6.436259
      1.0
      2.0
      3.0
      [0.7495892598619592, 4.413252742068436]
      stable spiral
      turing I oscillatory
      0.424730+0.000000j
      True
    

    

      1121752
      2
      0.01
      0.01
      35.095963
      42.009523
      204.670259
      129.529962
      196.206965
      0.243414
      0.020611
      0.020159
      7.591947
      1.0
      2.0
      3.0
      [49.09562577313732, 123.28756484848293]
      stable spiral
      turing I oscillatory
      0.000807+0.000000j
      True
    

    

      1133916
      0
      0.01
      0.01
      25.280346
      24.820603
      0.46665
      2.894328
      0.1715
      22.588552
      2.340128
      1.812301
      9.296574
      1.0
      2.0
      3.0
      [0.2034561068203429, 8.012134944940739]
      stable spiral
      turing I oscillatory
      0.095140+0.000000j
      True
    

    

      1195022
      4
      0.01
      0.01
      611.443333
      65.418422
      3.679404
      4.916145
      0.291868
      23.16549
      2.681936
      3.639532
      3.868892
      1.0
      2.0
      5.0
      [0.4073849406238525, 11.880455617100312]
      stable spiral
      turing I oscillatory
      0.143158+0.000000j
      True
    

    

      1251569
      4
      0.01
      0.01
      78.336332
      335.8188
      4.829453
      130.863529
      0.699689
      126.268829
      0.499399
      0.65867
      8.65381
      1.0
      2.0
      5.0
      [0.6885816883438481, 250.8581190221953]
      stable spiral
      turing I oscillatory
      0.046860+0.000000j
      True
    

    

      1271426
      4
      0.01
      0.01
      190.225628
      293.574451
      6.800965
      0.220735
      246.462242
      0.352554
      0.054968
      0.006626
      7.974703
      1.0
      2.0
      6.0
      [0.4134463429492179, 1.62368287660001]
      stable spiral
      turing I oscillatory
      0.001451+0.000000j
      True
    

    

      1282460
      3
      0.01
      0.01
      137.408687
      46.23023
      4.385336
      22.875974
      0.252308
      0.708833
      0.604388
      0.595292
      7.041691
      1.0
      2.0
      6.0
      [0.1833894275564431, 26.86238075319138]
      stable spiral
      turing I oscillatory
      0.054598+0.000000j
      True
    

    

      1363184
      1
      0.01
      0.01
      489.867101
      792.691596
      23.2304
      8.414381
      44.274836
      60.432306
      4.384669
      4.435087
      8.081678
      1.0
      2.0
      3.0
      [8.337167823322593, 6.122832994581747]
      stable spiral
      turing I oscillatory
      0.392614+0.000000j
      True
    

    

      1365378
      2
      0.01
      0.01
      47.893379
      303.586285
      12.846917
      59.298562
      4.406504
      2.498057
      0.171392
      0.241051
      4.8994
      1.0
      2.0
      3.0
      [0.7907720328139534, 39.33513345288904]
      stable spiral
      turing I oscillatory
      0.002308+0.000000j
      True
    

    

      1511110
      2
      0.01
      0.01
      197.016177
      14.839225
      21.290597
      70.581639
      1.527489
      213.061554
      0.026462
      0.022356
      6.577504
      1.0
      2.0
      3.0
      [2.4523664569276242, 478.67726622832197]
      stable spiral
      turing I oscillatory
      0.003142+0.000000j
      True
    

    

      1591223
      2
      0.01
      0.01
      181.448886
      124.238834
      1.907752
      0.94919
      1.236092
      0.733066
      8.468121
      8.174364
      4.305125
      1.0
      2.0
      3.0
      [0.28267661915408165, 0.7565643792046686]
      stable spiral
      turing I oscillatory
      0.281755+0.000000j
      True
    

    

      1690913
      2
      0.01
      0.01
      352.732001
      181.597206
      22.845293
      169.519337
      1.32443
      0.328719
      0.284128
      0.272296
      9.110464
      1.0
      2.0
      5.0
      [0.7135287798621495, 150.06077262200557]
      stable spiral
      turing I oscillatory
      0.085033+0.000000j
      True
    

    

      1695603
      2
      0.01
      0.01
      31.977446
      355.747002
      0.594938
      0.1548
      3.044665
      0.19553
      7.723634
      7.413257
      8.454431
      1.0
      2.0
      3.0
      [0.1617053445024249, 0.1363300826977407]
      stable spiral
      turing I oscillatory
      0.695926+0.000000j
      True
    

    

      1706579
      2
      0.01
      0.01
      59.659134
      58.375133
      19.491104
      58.357742
      3.484742
      203.245847
      0.084575
      0.074815
      9.42537
      1.0
      2.0
      3.0
      [1.028089729998471, 62.60937462185654]
      stable spiral
      turing I oscillatory
      0.004786+0.000000j
      True
    

    

      1710204
      2
      0.01
      0.01
      216.472116
      83.969955
      13.679493
      3.499946
      12.575576
      14.762697
      3.986566
      6.215158
      7.634227
      1.0
      2.0
      3.0
      [5.238181642480354, 1.9991380361740079]
      stable point
      turing I
      0.552865+0.000000j
      False
    

    

      1808247
      2
      0.01
      0.01
      115.560245
      388.299794
      6.223902
      2.576694
      12.162629
      66.473206
      2.598519
      2.727407
      6.893609
      1.0
      2.0
      3.0
      [1.4192455159140578, 1.9161763600605481]
      stable spiral
      turing I oscillatory
      0.380137+0.000000j
      True
    

    

      1809921
      2
      0.01
      0.01
      63.369102
      364.642716
      3.14131
      138.107548
      0.569865
      1.72784
      0.610343
      0.623043
      6.612335
      1.0
      2.0
      3.0
      [0.6477547781387554, 329.9323577408554]
      stable spiral
      turing I oscillatory
      0.056496+0.000000j
      True
    

    

      1814923
      2
      0.01
      0.01
      53.560559
      13.542736
      2.373041
      1.160449
      0.819207
      1.503472
      2.40326
      2.987094
      7.673755
      1.0
      2.0
      3.0
      [0.38006233679259854, 0.8063426130688415]
      stable spiral
      turing I oscillatory
      0.728068+0.000000j
      True
    

    

      1826994
      2
      0.01
      0.01
      41.29137
      10.931652
      8.881407
      0.595795
      18.624382
      0.235406
      1.207538
      1.252141
      8.378672
      1.0
      2.0
      3.0
      [4.054091455041588, 0.4029438580690021]
      stable spiral
      turing I oscillatory
      0.018427+0.000000j
      True
    

    

      1869288
      0
      0.01
      0.01
      727.246993
      34.303082
      4.047835
      1.469768
      0.212337
      1.200459
      1.293261
      0.817094
      9.675184
      1.0
      2.0
      1.0
      [0.03357005450353577, 1.0359803479365348]
      stable spiral
      turing I oscillatory
      0.140083+0.000000j
      True
    

    

      1874504
      1
      0.01
      0.01
      122.949334
      29.388223
      27.486708
      26.801067
      4.632377
      5.637666
      0.212463
      0.272714
      9.238316
      1.0
      2.0
      3.0
      [2.796877956370077, 28.825395553824976]
      stable spiral
      turing I oscillatory
      0.037788+0.000000j
      True
    

  



























In [ ]:



     


df.loc[1869288]



     






















    
    
Out[ ]:











  
    

      
      ba
      bb
      Va
      Vb
      kaa
      kba
      kab
      kbb
      mua
      mub
      d_B
      d_A
      n
      ss_n
      ss_list
      ss_class
      system_class
      maxeig
      complex_dispersion
    

    

      ss
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      0
      0.01
      0.01
      727.246993
      34.303082
      4.047835
      1.469768
      0.212337
      1.200459
      1.293261
      0.817094
      9.675184
      1.0
      2.0
      1.0
      [0.03357005450353577, 1.0359803479365348]
      stable spiral
      turing I oscillatory
      0.140083+0.000000j
      True
    

  



























In [ ]:



     


lsa_df= pickle.load( open(modellingpath + "/growth/out/analytical/lsa_dataframes/lsa_df_%s_variant%r_%rparametersets.pkl"%(circuit_n,variant,n_param_sets), "rb"))



     



















In [ ]:



     


lsa_df



     






















    
    
Out[ ]:











  
    

      
      
      ba
      bb
      Va
      Vb
      kaa
      kba
      kab
      kbb
      mua
      mub
      d_B
      d_A
      n
      ss_n
      ss_list
      ss_class
      system_class
      maxeig
      complex_dispersion
    

  
  
    

      1
      0
      0.01
      0.01
      30.117387
      106.659012
      101.031181
      0.110266
      3.006921
      0.370174
      0.021408
      0.524208
      0.002191
      1.0
      2.0
      2.0
      [0.467134082670621, 4.813937058901632]
      stable point
      simple stable
      -0.021409+0.000000j
      False
    

    

      1
      0.01
      0.01
      30.117387
      106.659012
      101.031181
      0.110266
      3.006921
      0.370174
      0.021408
      0.524208
      0.002191
      1.0
      2.0
      2.0
      [0.4646286711945737, 4.763717422192187]
      stable point
      simple stable
      -0.021409+0.000000j
      False
    

    

      2
      0
      0.01
      0.01
      840.234641
      175.845505
      0.393501
      1.111872
      0.193808
      0.300886
      0.027935
      0.00243
      9.639038
      1.0
      2.0
      1.0
      [0.3579847646089406, 55963.782124851234]
      stable point
      simple stable
      -0.002430+0.000000j
      False
    

    

      3
      0
      0.01
      0.01
      237.15197
      13.70142
      3.070877
      0.525973
      26.087472
      2.467677
      3.855762
      0.001846
      0.14785
      1.0
      2.0
      1.0
      [0.0025937558718074523, 5.417458575619242]
      stable point
      simple stable
      -0.001846+0.000000j
      False
    

    

      4
      0
      0.01
      0.01
      18.501211
      11.102393
      0.799942
      81.252957
      131.624941
      0.655627
      0.029659
      0.062291
      0.027914
      1.0
      2.0
      1.0
      [192.6888144770627, 121.68785390465611]
      stable spiral
      simple stable
      -0.045975+0.036810j
      True
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      1999998
      0
      0.01
      0.01
      389.627545
      32.272899
      69.113253
      0.596815
      0.125721
      1.788481
      5.328112
      0.002996
      4.672408
      1.0
      2.0
      1.0
      [0.0018768374787926174, 5.737724447115397]
      stable point
      simple stable
      -0.002996+0.000000j
      False
    

    

      1999999
      0
      0.01
      0.01
      410.030107
      738.276678
      5.093089
      61.629085
      36.580175
      2.832781
      12.822408
      3.807407
      0.008583
      1.0
      2.0
      3.0
      [0.0007800771696116996, 0.0026264818297415025]
      stable point
      simple stable
      -3.807407+0.000000j
      False
    

    

      1
      0.01
      0.01
      410.030107
      738.276678
      5.093089
      61.629085
      36.580175
      2.832781
      12.822408
      3.807407
      0.008583
      1.0
      2.0
      3.0
      [19.844543061701074, 44.09316650087448]
      stable spiral
      simple stable
      -5.078776+0.000000j
      True
    

    

      2
      0.01
      0.01
      410.030107
      738.276678
      5.093089
      61.629085
      36.580175
      2.832781
      12.822408
      3.807407
      0.008583
      1.0
      2.0
      3.0
      [0.8320563116927623, 0.10289748702093715]
      unstable point
      simple unstable
      12.132485+0.000000j
      False
    

    

      2000000
      0
      0.01
      0.01
      44.867121
      95.558215
      23.542469
      17.933697
      17.123672
      20.034032
      2.052339
      0.057336
      0.001193
      1.0
      2.0
      1.0
      [0.0048732919851288865, 0.1744300725036112]
      stable point
      simple stable
      -0.057336+0.000000j
      False
    

  



2663471 rows × 19 columns